A Systematic Approach for Enhancing Software Defect Prediction Using Machine Learning

In the modern world of software development, ensuring reliability and performance is of paramount importance. However, despite the best efforts from the developers, software defects can still emerge, causing frustration and wasted resources. Due to the numerous defects found during the software development process, researchers have developed numerous ways for defect prediction models. However, these models cut down the time and expense of development when problems in a concurrent software product are anticipated. Due to the increased amount of defects brought on by software complexity, manual defect detection can become an extremely time-consuming procedure. This encouraged researchers to create methods for the automatic detection of software defects. The study of this paper has shown that a combination of machine learning algorithms could be applied effectively for software defect prediction. Interestingly, the combination of Artificial Neural Network and Random Forest classifier has been performed with the mean accuracy of 91%, while the hyper-parameter optimization model classifier has been performed with the mean accuracy of 83%, 83%, 84%, 77% and 80% for Support Vector Machine, Random Forest, Logistic Regression, Naive Bayes Gaussian and Decision Tree, respectively. These findings have demonstrated the potential of Machine Learning in the area of software development.

[1]  A. Akbulut,et al.  Machine Learning-Based Software Defect Prediction for Mobile Applications: A Systematic Literature Review , 2022, Sensors.

[2]  Ahmed Sharaf Eldin,et al.  Software defect prediction using hybrid model (CBIL) of convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM) , 2021, PeerJ Comput. Sci..

[3]  N. Ziviani,et al.  Predicting Software Defects with Explainable Machine Learning , 2020, SBQS.

[4]  Mitt Shah,et al.  A Review On Software Defects Prediction Methods , 2020, ArXiv.

[5]  M. Hammad,et al.  Software Defects Prediction using Machine Learning Algorithms , 2020, 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI).

[6]  Mohammad Amimul Ihsan Aquil,et al.  Predicting Software Defects using Machine Learning Techniques , 2020 .

[7]  Abdullah Alsaeedi,et al.  Software Defect Prediction Using Supervised Machine Learning and Ensemble Techniques: A Comparative Study , 2019, Journal of Software Engineering and Applications.

[8]  Mahmoud Al-Ayyoub,et al.  Dynamic Detection of Software Defects Using Supervised Learning Techniques , 2019, Int. J. Commun. Networks Inf. Secur..

[9]  William Stafford Noble,et al.  Support vector machine , 2013 .

[10]  Abdulaziz Alhumam.  Effective Prediction of Software Defects using Random-tree Entropy based Feature Selection Framework , 2022, International Journal of Advanced Computer Science and Applications.

[11]  Sagheer Abbas,et al.  Machine Learning Empowered Software Defect Prediction System , 2022, Intelligent Automation & Soft Computing.

[12]  Muhammad Iqbal,et al.  Software Defect Prediction Using Supervised Machine Learning Techniques: A Systematic Literature Review , 2021, Intelligent Automation & Soft Computing.

[13]  A. SanusiB..  Software Defect Prediction System using Machine Learning based Algorithms , 2019 .

[14]  Mustafa Hammad,et al.  Software Bug Prediction using Machine Learning Approach , 2018 .

[15]  Hong Zhou,et al.  Artificial Neural Network , 2020, Encyclopedia of GIS.

[16]  Kristin L. Sainani,et al.  Logistic Regression , 2014, PM & R : the journal of injury, function, and rehabilitation.

[17]  Johannes Fürnkranz,et al.  Decision Tree , 2010, Encyclopedia of Machine Learning and Data Mining.